Entropy‐Based Spatial Interaction Models for Trip Distribution. 基于熵的空间相互作用模型在出行分布中的应用
Bibliographic record
Abstract
Wilson's use of entropy‐maximization techniques to derive a family of spatial interaction models was a major innovation in urban and regional modeling. The work elegantly linked methods for transportation analysis and regional economics into a unified framework. One version, the doubly constrained spatial interaction model, is closely related to the transportation problem of linear programming and other existing trip distribution techniques. This article traces some of these connections, particularly the sense in which an entropy model with an average trip length constraint can be seen as a relaxation of a least cost solution to a linear program. These ideas have renewed significance in the context of studies of simulation models for regional economies. Wilson's developments therefore provided a unifying basis for the work of very creative urban and regional modelers of that time (e.g., Alonso, Batty, Evans, Harris, Herbert, Lakshmanan, Stevens, Webber) and indicate the lasting and significant influence of his insight.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".